Overview

Dataset statistics

Number of variables22
Number of observations5115
Missing cells191
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory879.3 KiB
Average record size in memory176.0 B

Variable types

Categorical8
Numeric14

Alerts

username has a high cardinality: 1972 distinct valuesHigh cardinality
title has a high cardinality: 2606 distinct valuesHigh cardinality
genre has a high cardinality: 1714 distinct valuesHigh cardinality
Duration has a high cardinality: 217 distinct valuesHigh cardinality
score is highly overall correlated with scored_by and 2 other fieldsHigh correlation
scored_by is highly overall correlated with score and 2 other fieldsHigh correlation
rank is highly overall correlated with score and 2 other fieldsHigh correlation
popularity is highly overall correlated with score and 2 other fieldsHigh correlation
Ranked is highly overall correlated with FavoritesHigh correlation
Popularity is highly overall correlated with Favorites and 1 other fieldsHigh correlation
Favorites is highly overall correlated with Ranked and 2 other fieldsHigh correlation
Dropped is highly overall correlated with Popularity and 1 other fieldsHigh correlation
rank has 121 (2.4%) missing valuesMissing
my_score has 1954 (38.2%) zerosZeros
Ranked has 368 (7.2%) zerosZeros
Favorites has 464 (9.1%) zerosZeros

Reproduction

Analysis started2023-06-29 02:40:59.273597
Analysis finished2023-06-29 02:42:10.553778
Duration1 minute and 11.28 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

username
Categorical

Distinct1972
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
DeadlyKizuna
 
39
canc
 
33
SakurasDreams
 
21
#NAME?
 
20
MistButterfly
 
20
Other values (1967)
4982 

Length

Max length16
Median length13
Mean length8.8232649
Min length2

Characters and Unicode

Total characters45131
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique718 ?
Unique (%)14.0%

Sample

1st rowkarthiga
2nd rowRedvelvetDaisuki
3rd rowRedvelvetDaisuki
4th rowRedvelvetDaisuki
5th rowDamonashu

Common Values

ValueCountFrequency (%)
DeadlyKizuna 39
 
0.8%
canc 33
 
0.6%
SakurasDreams 21
 
0.4%
#NAME? 20
 
0.4%
MistButterfly 20
 
0.4%
ecoute 20
 
0.4%
domine 20
 
0.4%
Mikufanfromhell 19
 
0.4%
Infernus_ 19
 
0.4%
Pullman 19
 
0.4%
Other values (1962) 4885
95.5%

Length

2023-06-29T02:42:10.924800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deadlykizuna 39
 
0.8%
canc 33
 
0.6%
sakurasdreams 21
 
0.4%
name 20
 
0.4%
mistbutterfly 20
 
0.4%
ecoute 20
 
0.4%
domine 20
 
0.4%
mikufanfromhell 19
 
0.4%
infernus 19
 
0.4%
pullman 19
 
0.4%
Other values (1962) 4885
95.5%

Most occurring characters

ValueCountFrequency (%)
a 4469
 
9.9%
e 3546
 
7.9%
i 3267
 
7.2%
r 2746
 
6.1%
n 2685
 
5.9%
o 2536
 
5.6%
u 1900
 
4.2%
s 1870
 
4.1%
l 1621
 
3.6%
t 1501
 
3.3%
Other values (56) 18990
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36233
80.3%
Uppercase Letter 6070
 
13.4%
Decimal Number 1915
 
4.2%
Connector Punctuation 508
 
1.1%
Dash Punctuation 365
 
0.8%
Other Punctuation 40
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4469
12.3%
e 3546
 
9.8%
i 3267
 
9.0%
r 2746
 
7.6%
n 2685
 
7.4%
o 2536
 
7.0%
u 1900
 
5.2%
s 1870
 
5.2%
l 1621
 
4.5%
t 1501
 
4.1%
Other values (16) 10092
27.9%
Uppercase Letter
ValueCountFrequency (%)
S 625
 
10.3%
M 519
 
8.6%
A 491
 
8.1%
K 423
 
7.0%
D 341
 
5.6%
T 308
 
5.1%
R 292
 
4.8%
N 270
 
4.4%
L 243
 
4.0%
I 228
 
3.8%
Other values (16) 2330
38.4%
Decimal Number
ValueCountFrequency (%)
1 296
15.5%
0 273
14.3%
9 234
12.2%
2 227
11.9%
3 191
10.0%
4 178
9.3%
8 154
8.0%
7 154
8.0%
5 116
 
6.1%
6 92
 
4.8%
Other Punctuation
ValueCountFrequency (%)
? 20
50.0%
# 20
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 508
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 365
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42303
93.7%
Common 2828
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4469
 
10.6%
e 3546
 
8.4%
i 3267
 
7.7%
r 2746
 
6.5%
n 2685
 
6.3%
o 2536
 
6.0%
u 1900
 
4.5%
s 1870
 
4.4%
l 1621
 
3.8%
t 1501
 
3.5%
Other values (42) 16162
38.2%
Common
ValueCountFrequency (%)
_ 508
18.0%
- 365
12.9%
1 296
10.5%
0 273
9.7%
9 234
8.3%
2 227
8.0%
3 191
 
6.8%
4 178
 
6.3%
8 154
 
5.4%
7 154
 
5.4%
Other values (4) 248
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4469
 
9.9%
e 3546
 
7.9%
i 3267
 
7.2%
r 2746
 
6.1%
n 2685
 
5.9%
o 2536
 
5.6%
u 1900
 
4.2%
s 1870
 
4.1%
l 1621
 
3.6%
t 1501
 
3.3%
Other values (56) 18990
42.1%

anime_id
Real number (ℝ)

Distinct2606
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11051.668
Minimum1
Maximum37537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:11.352786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile154.7
Q11571
median6880
Q318153
95-th percentile33359.1
Maximum37537
Range37536
Interquartile range (IQR)16582

Descriptive statistics

Standard deviation11125.28
Coefficient of variation (CV)1.0066607
Kurtosis-0.51190004
Mean11051.668
Median Absolute Deviation (MAD)6067
Skewness0.89281774
Sum56529282
Variance1.2377185 × 108
MonotonicityNot monotonic
2023-06-29T02:42:11.819792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9253 16
 
0.3%
225 14
 
0.3%
21 13
 
0.3%
1735 12
 
0.2%
16498 11
 
0.2%
71 11
 
0.2%
12189 11
 
0.2%
31240 10
 
0.2%
1535 10
 
0.2%
813 10
 
0.2%
Other values (2596) 4997
97.7%
ValueCountFrequency (%)
1 7
0.1%
5 3
 
0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
19 8
0.2%
20 8
0.2%
21 13
0.3%
22 4
 
0.1%
ValueCountFrequency (%)
37537 1
 
< 0.1%
37505 1
 
< 0.1%
37277 1
 
< 0.1%
37258 1
 
< 0.1%
37078 2
< 0.1%
36949 3
0.1%
36848 1
 
< 0.1%
36828 1
 
< 0.1%
36754 1
 
< 0.1%
36729 1
 
< 0.1%

my_score
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5710655
Minimum0
Maximum10
Zeros1954
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:12.423793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.8549908
Coefficient of variation (CV)0.84334621
Kurtosis-1.6754514
Mean4.5710655
Median Absolute Deviation (MAD)3
Skewness-0.15314681
Sum23381
Variance14.860954
MonotonicityNot monotonic
2023-06-29T02:42:12.799799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1954
38.2%
8 799
15.6%
7 724
 
14.2%
9 477
 
9.3%
10 365
 
7.1%
6 359
 
7.0%
5 241
 
4.7%
4 104
 
2.0%
3 44
 
0.9%
1 25
 
0.5%
ValueCountFrequency (%)
0 1954
38.2%
1 25
 
0.5%
2 23
 
0.4%
3 44
 
0.9%
4 104
 
2.0%
5 241
 
4.7%
6 359
 
7.0%
7 724
 
14.2%
8 799
15.6%
9 477
 
9.3%
ValueCountFrequency (%)
10 365
7.1%
9 477
9.3%
8 799
15.6%
7 724
14.2%
6 359
7.0%
5 241
 
4.7%
4 104
 
2.0%
3 44
 
0.9%
2 23
 
0.4%
1 25
 
0.5%

user_id
Real number (ℝ)

Distinct1976
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1530228.8
Minimum1
Maximum7122186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:13.254799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22656.7
Q1141614
median413669
Q32832533
95-th percentile5368578.3
Maximum7122186
Range7122185
Interquartile range (IQR)2690919

Descriptive statistics

Standard deviation1877542.8
Coefficient of variation (CV)1.2269687
Kurtosis-0.11897255
Mean1530228.8
Median Absolute Deviation (MAD)380352
Skewness1.1202016
Sum7.8271203 × 109
Variance3.5251669 × 1012
MonotonicityNot monotonic
2023-06-29T02:42:13.785811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1237755 39
 
0.8%
132251 33
 
0.6%
186962 21
 
0.4%
288152 20
 
0.4%
2485327 20
 
0.4%
717529 20
 
0.4%
169608 19
 
0.4%
294198 19
 
0.4%
430775 19
 
0.4%
562963 17
 
0.3%
Other values (1966) 4888
95.6%
ValueCountFrequency (%)
1 2
< 0.1%
988 2
< 0.1%
1056 1
 
< 0.1%
1542 3
0.1%
2380 2
< 0.1%
2717 1
 
< 0.1%
2930 3
0.1%
3175 3
0.1%
3290 2
< 0.1%
3341 3
0.1%
ValueCountFrequency (%)
7122186 1
 
< 0.1%
7024243 1
 
< 0.1%
6980415 2
< 0.1%
6955850 3
0.1%
6923415 1
 
< 0.1%
6907514 3
0.1%
6894967 1
 
< 0.1%
6859227 1
 
< 0.1%
6854056 1
 
< 0.1%
6819602 1
 
< 0.1%

gender
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
Male
3888 
Female
1192 
Non-Binary
 
35

Length

Max length10
Median length4
Mean length4.5071359
Min length4

Characters and Unicode

Total characters23054
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 3888
76.0%
Female 1192
 
23.3%
Non-Binary 35
 
0.7%

Length

2023-06-29T02:42:14.273807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T02:42:14.653811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
male 3888
76.0%
female 1192
 
23.3%
non-binary 35
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 6272
27.2%
a 5115
22.2%
l 5080
22.0%
M 3888
16.9%
F 1192
 
5.2%
m 1192
 
5.2%
n 70
 
0.3%
N 35
 
0.2%
o 35
 
0.2%
- 35
 
0.2%
Other values (4) 140
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17869
77.5%
Uppercase Letter 5150
 
22.3%
Dash Punctuation 35
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6272
35.1%
a 5115
28.6%
l 5080
28.4%
m 1192
 
6.7%
n 70
 
0.4%
o 35
 
0.2%
i 35
 
0.2%
r 35
 
0.2%
y 35
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
M 3888
75.5%
F 1192
 
23.1%
N 35
 
0.7%
B 35
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23019
99.8%
Common 35
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6272
27.2%
a 5115
22.2%
l 5080
22.1%
M 3888
16.9%
F 1192
 
5.2%
m 1192
 
5.2%
n 70
 
0.3%
N 35
 
0.2%
o 35
 
0.2%
B 35
 
0.2%
Other values (3) 105
 
0.5%
Common
ValueCountFrequency (%)
- 35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6272
27.2%
a 5115
22.2%
l 5080
22.0%
M 3888
16.9%
F 1192
 
5.2%
m 1192
 
5.2%
n 70
 
0.3%
N 35
 
0.2%
o 35
 
0.2%
- 35
 
0.2%
Other values (4) 140
 
0.6%

title
Categorical

Distinct2606
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
Steins;Gate
 
16
Dragon Ball GT
 
14
One Piece
 
13
Naruto: Shippuuden
 
12
Shingeki no Kyojin
 
11
Other values (2601)
5049 

Length

Max length98
Median length69
Mean length23.257478
Min length1

Characters and Unicode

Total characters118962
Distinct characters110
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1488 ?
Unique (%)29.1%

Sample

1st rowOne Piece
2nd rowGintama
3rd rowCoppelion
4th rowPing Pong The Animation
5th rowGedo Senki

Common Values

ValueCountFrequency (%)
Steins;Gate 16
 
0.3%
Dragon Ball GT 14
 
0.3%
One Piece 13
 
0.3%
Naruto: Shippuuden 12
 
0.2%
Shingeki no Kyojin 11
 
0.2%
Full Metal Panic! 11
 
0.2%
Hyouka 11
 
0.2%
Re:Zero kara Hajimeru Isekai Seikatsu 10
 
0.2%
Death Note 10
 
0.2%
Dragon Ball Z 10
 
0.2%
Other values (2596) 4997
97.7%

Length

2023-06-29T02:42:15.107810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 1436
 
7.5%
the 296
 
1.6%
movie 228
 
1.2%
to 223
 
1.2%
166
 
0.9%
ga 145
 
0.8%
wa 141
 
0.7%
ni 139
 
0.7%
of 123
 
0.6%
wo 107
 
0.6%
Other values (3967) 16056
84.2%

Most occurring characters

ValueCountFrequency (%)
13945
 
11.7%
a 10511
 
8.8%
o 9631
 
8.1%
i 8272
 
7.0%
e 7780
 
6.5%
n 7476
 
6.3%
u 6158
 
5.2%
r 4473
 
3.8%
t 4085
 
3.4%
s 3892
 
3.3%
Other values (100) 42739
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83326
70.0%
Uppercase Letter 16790
 
14.1%
Space Separator 13945
 
11.7%
Other Punctuation 2857
 
2.4%
Decimal Number 969
 
0.8%
Dash Punctuation 623
 
0.5%
Other Symbol 166
 
0.1%
Close Punctuation 129
 
0.1%
Open Punctuation 129
 
0.1%
Other Number 15
 
< 0.1%
Other values (2) 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10511
12.6%
o 9631
11.6%
i 8272
9.9%
e 7780
9.3%
n 7476
9.0%
u 6158
 
7.4%
r 4473
 
5.4%
t 4085
 
4.9%
s 3892
 
4.7%
k 3317
 
4.0%
Other values (24) 17731
21.3%
Uppercase Letter
ValueCountFrequency (%)
S 2283
13.6%
K 1403
 
8.4%
M 1274
 
7.6%
T 1114
 
6.6%
B 949
 
5.7%
A 915
 
5.4%
H 884
 
5.3%
D 864
 
5.1%
G 797
 
4.7%
O 725
 
4.3%
Other values (17) 5582
33.2%
Other Punctuation
ValueCountFrequency (%)
: 1386
48.5%
! 745
26.1%
. 237
 
8.3%
& 89
 
3.1%
; 89
 
3.1%
, 82
 
2.9%
? 58
 
2.0%
/ 57
 
2.0%
# 54
 
1.9%
" 28
 
1.0%
Other values (5) 32
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 266
27.5%
0 202
20.8%
3 142
14.7%
1 137
14.1%
9 77
 
7.9%
4 44
 
4.5%
5 35
 
3.6%
7 23
 
2.4%
6 22
 
2.3%
8 21
 
2.2%
Other Symbol
ValueCountFrequency (%)
117
70.5%
27
 
16.3%
7
 
4.2%
5
 
3.0%
° 3
 
1.8%
2
 
1.2%
2
 
1.2%
2
 
1.2%
1
 
0.6%
Math Symbol
ValueCountFrequency (%)
+ 5
41.7%
3
25.0%
= 3
25.0%
1
 
8.3%
Other Number
ValueCountFrequency (%)
½ 7
46.7%
³ 5
33.3%
² 3
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 621
99.7%
2
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 127
98.4%
] 2
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 127
98.4%
[ 2
 
1.6%
Space Separator
ValueCountFrequency (%)
13945
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100114
84.2%
Common 18846
 
15.8%
Greek 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10511
 
10.5%
o 9631
 
9.6%
i 8272
 
8.3%
e 7780
 
7.8%
n 7476
 
7.5%
u 6158
 
6.2%
r 4473
 
4.5%
t 4085
 
4.1%
s 3892
 
3.9%
k 3317
 
3.3%
Other values (50) 34519
34.5%
Common
ValueCountFrequency (%)
13945
74.0%
: 1386
 
7.4%
! 745
 
4.0%
- 621
 
3.3%
2 266
 
1.4%
. 237
 
1.3%
0 202
 
1.1%
3 142
 
0.8%
1 137
 
0.7%
) 127
 
0.7%
Other values (39) 1038
 
5.5%
Greek
ValueCountFrequency (%)
Ψ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118725
99.8%
Misc Symbols 156
 
0.1%
None 55
 
< 0.1%
Punctuation 15
 
< 0.1%
Geometric Shapes 5
 
< 0.1%
Math Operators 4
 
< 0.1%
Control Pictures 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13945
 
11.7%
a 10511
 
8.9%
o 9631
 
8.1%
i 8272
 
7.0%
e 7780
 
6.6%
n 7476
 
6.3%
u 6158
 
5.2%
r 4473
 
3.8%
t 4085
 
3.4%
s 3892
 
3.3%
Other values (74) 42502
35.8%
Misc Symbols
ValueCountFrequency (%)
117
75.0%
27
 
17.3%
7
 
4.5%
5
 
3.2%
None
ValueCountFrequency (%)
ä 18
32.7%
½ 7
 
12.7%
é 6
 
10.9%
³ 5
 
9.1%
² 3
 
5.5%
° 3
 
5.5%
ß 3
 
5.5%
Ψ 2
 
3.6%
è 2
 
3.6%
â 2
 
3.6%
Other values (4) 4
 
7.3%
Punctuation
ValueCountFrequency (%)
13
86.7%
2
 
13.3%
Math Operators
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Geometric Shapes
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
Control Pictures
ValueCountFrequency (%)
2
100.0%

type
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
TV
3316 
OVA
656 
Movie
638 
Special
387 
ONA
 
87

Length

Max length7
Median length2
Mean length2.9159335
Min length2

Characters and Unicode

Total characters14915
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTV
2nd rowTV
3rd rowTV
4th rowTV
5th rowMovie

Common Values

ValueCountFrequency (%)
TV 3316
64.8%
OVA 656
 
12.8%
Movie 638
 
12.5%
Special 387
 
7.6%
ONA 87
 
1.7%
Music 31
 
0.6%

Length

2023-06-29T02:42:15.343816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T02:42:15.563816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
tv 3316
64.8%
ova 656
 
12.8%
movie 638
 
12.5%
special 387
 
7.6%
ona 87
 
1.7%
music 31
 
0.6%

Most occurring characters

ValueCountFrequency (%)
V 3972
26.6%
T 3316
22.2%
i 1056
 
7.1%
e 1025
 
6.9%
A 743
 
5.0%
O 743
 
5.0%
M 669
 
4.5%
o 638
 
4.3%
v 638
 
4.3%
c 418
 
2.8%
Other values (7) 1697
11.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9917
66.5%
Lowercase Letter 4998
33.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1056
21.1%
e 1025
20.5%
o 638
12.8%
v 638
12.8%
c 418
 
8.4%
p 387
 
7.7%
a 387
 
7.7%
l 387
 
7.7%
u 31
 
0.6%
s 31
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
V 3972
40.1%
T 3316
33.4%
A 743
 
7.5%
O 743
 
7.5%
M 669
 
6.7%
S 387
 
3.9%
N 87
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 14915
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 3972
26.6%
T 3316
22.2%
i 1056
 
7.1%
e 1025
 
6.9%
A 743
 
5.0%
O 743
 
5.0%
M 669
 
4.5%
o 638
 
4.3%
v 638
 
4.3%
c 418
 
2.8%
Other values (7) 1697
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V 3972
26.6%
T 3316
22.2%
i 1056
 
7.1%
e 1025
 
6.9%
A 743
 
5.0%
O 743
 
5.0%
M 669
 
4.5%
o 638
 
4.3%
v 638
 
4.3%
c 418
 
2.8%
Other values (7) 1697
11.4%

source
Categorical

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
Manga
2211 
Original
904 
Light novel
761 
Visual novel
340 
Unknown
251 
Other values (9)
648 

Length

Max length13
Median length12
Mean length7.1546432
Min length4

Characters and Unicode

Total characters36596
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManga
2nd rowManga
3rd rowManga
4th rowManga
5th rowNovel

Common Values

ValueCountFrequency (%)
Manga 2211
43.2%
Original 904
17.7%
Light novel 761
 
14.9%
Visual novel 340
 
6.6%
Unknown 251
 
4.9%
Game 198
 
3.9%
Novel 176
 
3.4%
4-koma manga 103
 
2.0%
Web manga 70
 
1.4%
Other 65
 
1.3%
Other values (4) 36
 
0.7%

Length

2023-06-29T02:42:15.764813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manga 2387
37.3%
novel 1277
19.9%
original 904
 
14.1%
light 761
 
11.9%
visual 340
 
5.3%
unknown 251
 
3.9%
game 208
 
3.2%
4-koma 103
 
1.6%
web 70
 
1.1%
other 65
 
1.0%
Other values (4) 36
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 6342
17.3%
n 5145
14.1%
g 4065
11.1%
i 2932
 
8.0%
l 2524
 
6.9%
M 2228
 
6.1%
o 1643
 
4.5%
e 1620
 
4.4%
1287
 
3.5%
v 1277
 
3.5%
Other values (23) 7533
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30091
82.2%
Uppercase Letter 5012
 
13.7%
Space Separator 1287
 
3.5%
Decimal Number 103
 
0.3%
Dash Punctuation 103
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6342
21.1%
n 5145
17.1%
g 4065
13.5%
i 2932
9.7%
l 2524
 
8.4%
o 1643
 
5.5%
e 1620
 
5.4%
v 1277
 
4.2%
r 979
 
3.3%
t 829
 
2.8%
Other values (9) 2735
9.1%
Uppercase Letter
ValueCountFrequency (%)
M 2228
44.5%
O 969
19.3%
L 761
 
15.2%
V 340
 
6.8%
U 251
 
5.0%
G 198
 
4.0%
N 176
 
3.5%
W 70
 
1.4%
C 10
 
0.2%
B 6
 
0.1%
Space Separator
ValueCountFrequency (%)
1287
100.0%
Decimal Number
ValueCountFrequency (%)
4 103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35103
95.9%
Common 1493
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6342
18.1%
n 5145
14.7%
g 4065
11.6%
i 2932
8.4%
l 2524
 
7.2%
M 2228
 
6.3%
o 1643
 
4.7%
e 1620
 
4.6%
v 1277
 
3.6%
r 979
 
2.8%
Other values (20) 6348
18.1%
Common
ValueCountFrequency (%)
1287
86.2%
4 103
 
6.9%
- 103
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6342
17.3%
n 5145
14.1%
g 4065
11.1%
i 2932
 
8.0%
l 2524
 
6.9%
M 2228
 
6.1%
o 1643
 
4.5%
e 1620
 
4.4%
1287
 
3.5%
v 1277
 
3.5%
Other values (23) 7533
20.6%

score
Real number (ℝ)

Distinct378
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4943011
Minimum2.33
Maximum9.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:16.011822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.33
5-th percentile6.23
Q17.05
median7.53
Q38.01
95-th percentile8.55
Maximum9.25
Range6.92
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.74849634
Coefficient of variation (CV)0.099875403
Kurtosis2.000267
Mean7.4943011
Median Absolute Deviation (MAD)0.48
Skewness-0.73095524
Sum38333.35
Variance0.56024677
MonotonicityNot monotonic
2023-06-29T02:42:16.275821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 65
 
1.3%
7.41 61
 
1.2%
7.45 53
 
1.0%
7.4 49
 
1.0%
7.63 43
 
0.8%
7.64 42
 
0.8%
7.72 42
 
0.8%
7.47 41
 
0.8%
7.32 40
 
0.8%
7.38 40
 
0.8%
Other values (368) 4639
90.7%
ValueCountFrequency (%)
2.33 1
< 0.1%
2.88 1
< 0.1%
3.16 1
< 0.1%
3.54 2
< 0.1%
3.65 1
< 0.1%
3.71 1
< 0.1%
3.76 1
< 0.1%
4.03 1
< 0.1%
4.27 1
< 0.1%
4.37 1
< 0.1%
ValueCountFrequency (%)
9.25 8
0.2%
9.19 4
 
0.1%
9.15 5
 
0.1%
9.14 16
0.3%
9.11 9
0.2%
9.1 2
 
< 0.1%
9.07 2
 
< 0.1%
9.04 4
 
0.1%
9.02 3
 
0.1%
9.01 16
0.3%

scored_by
Real number (ℝ)

Distinct2538
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99663.424
Minimum200
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:16.584820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile1566.1
Q113727
median46179
Q3120583.5
95-th percentile403377
Maximum1000000
Range999800
Interquartile range (IQR)106856.5

Descriptive statistics

Standard deviation140491.1
Coefficient of variation (CV)1.4096556
Kurtosis9.5606173
Mean99663.424
Median Absolute Deviation (MAD)39452
Skewness2.729734
Sum5.0977842 × 108
Variance1.973775 × 1010
MonotonicityNot monotonic
2023-06-29T02:42:16.923822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
563857 16
 
0.3%
205925 14
 
0.3%
423868 13
 
0.3%
385179 12
 
0.2%
940211 11
 
0.2%
167633 11
 
0.2%
241642 11
 
0.2%
1000000 10
 
0.2%
514656 10
 
0.2%
171370 10
 
0.2%
Other values (2528) 4997
97.7%
ValueCountFrequency (%)
200 1
< 0.1%
204 1
< 0.1%
208 1
< 0.1%
212 1
< 0.1%
215 1
< 0.1%
217 1
< 0.1%
222 1
< 0.1%
231 1
< 0.1%
242 2
< 0.1%
244 1
< 0.1%
ValueCountFrequency (%)
1000000 10
0.2%
940211 11
0.2%
915986 7
0.1%
733592 8
0.2%
691845 4
 
0.1%
659308 3
 
0.1%
648605 8
0.2%
641851 8
0.2%
627740 9
0.2%
623227 5
0.1%

rank
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2291
Distinct (%)45.9%
Missing121
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean2131.9656
Minimum1
Maximum9517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:17.177824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile78.3
Q1532
median1485
Q33190
95-th percentile6401.75
Maximum9517
Range9516
Interquartile range (IQR)2658

Descriptive statistics

Standard deviation2031.8897
Coefficient of variation (CV)0.95305936
Kurtosis1.0867945
Mean2131.9656
Median Absolute Deviation (MAD)1126
Skewness1.2556719
Sum10647036
Variance4128575.8
MonotonicityNot monotonic
2023-06-29T02:42:17.486833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 16
 
0.3%
4883 14
 
0.3%
91 13
 
0.3%
337 13
 
0.3%
120 13
 
0.3%
372 12
 
0.2%
183 12
 
0.2%
394 12
 
0.2%
985 11
 
0.2%
110 11
 
0.2%
Other values (2281) 4867
95.2%
(Missing) 121
 
2.4%
ValueCountFrequency (%)
1 8
0.2%
2 4
 
0.1%
3 3
 
0.1%
4 2
 
< 0.1%
5 16
0.3%
7 4
 
0.1%
8 5
 
0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
12 4
 
0.1%
ValueCountFrequency (%)
9517 1
< 0.1%
9514 1
< 0.1%
9511 1
< 0.1%
9486 1
< 0.1%
9485 1
< 0.1%
9484 1
< 0.1%
9472 1
< 0.1%
9471 1
< 0.1%
9445 1
< 0.1%
9416 1
< 0.1%

popularity
Real number (ℝ)

Distinct2385
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.0923
Minimum1
Maximum10080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:17.805835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q1298.5
median871
Q32083.5
95-th percentile5211.9
Maximum10080
Range10079
Interquartile range (IQR)1785

Descriptive statistics

Standard deviation1707.7129
Coefficient of variation (CV)1.1368895
Kurtosis3.6172474
Mean1502.0923
Median Absolute Deviation (MAD)699
Skewness1.8503693
Sum7683202
Variance2916283.2
MonotonicityNot monotonic
2023-06-29T02:42:18.076835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 18
 
0.4%
8 16
 
0.3%
204 14
 
0.3%
414 13
 
0.3%
240 12
 
0.2%
146 12
 
0.2%
20 12
 
0.2%
455 11
 
0.2%
73 11
 
0.2%
339 11
 
0.2%
Other values (2375) 4985
97.5%
ValueCountFrequency (%)
1 10
0.2%
2 11
0.2%
3 7
0.1%
4 8
0.2%
5 4
 
0.1%
6 3
 
0.1%
7 8
0.2%
8 16
0.3%
9 9
0.2%
10 8
0.2%
ValueCountFrequency (%)
10080 1
< 0.1%
9881 1
< 0.1%
9510 1
< 0.1%
9461 1
< 0.1%
9455 2
< 0.1%
9430 1
< 0.1%
9381 1
< 0.1%
9299 1
< 0.1%
9283 1
< 0.1%
9220 1
< 0.1%

genre
Categorical

Distinct1714
Distinct (%)33.5%
Missing1
Missing (%)< 0.1%
Memory size40.1 KiB
Hentai
 
66
Comedy
 
38
Slice of Life, Comedy, School
 
28
Slice of Life, Comedy
 
27
Music
 
24
Other values (1709)
4931 

Length

Max length93
Median length71
Mean length37.38991
Min length4

Characters and Unicode

Total characters191212
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique736 ?
Unique (%)14.4%

Sample

1st rowAction, Adventure, Comedy, Super Power, Drama, Fantasy, Shounen
2nd rowAction, Sci-Fi, Comedy, Historical, Parody, Samurai, Shounen
3rd rowAction, Sci-Fi, Seinen
4th rowSports, Psychological, Drama, Seinen
5th rowAdventure, Fantasy, Magic

Common Values

ValueCountFrequency (%)
Hentai 66
 
1.3%
Comedy 38
 
0.7%
Slice of Life, Comedy, School 28
 
0.5%
Slice of Life, Comedy 27
 
0.5%
Music 24
 
0.5%
Slice of Life, Comedy, Romance, School 23
 
0.4%
Action, Adventure, Comedy, Fantasy, Martial Arts, Shounen, Super Power 21
 
0.4%
Action, Seinen 20
 
0.4%
Action, Adventure, Comedy, Super Power, Martial Arts, Shounen 20
 
0.4%
Comedy, Slice of Life 19
 
0.4%
Other values (1704) 4828
94.4%

Length

2023-06-29T02:42:18.348831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy 2506
 
10.3%
action 2123
 
8.7%
drama 1369
 
5.6%
romance 1351
 
5.6%
fantasy 1324
 
5.4%
shounen 1318
 
5.4%
adventure 1182
 
4.9%
supernatural 1129
 
4.6%
school 1107
 
4.5%
sci-fi 974
 
4.0%
Other values (36) 9947
40.9%

Most occurring characters

ValueCountFrequency (%)
19216
 
10.0%
, 16984
 
8.9%
e 14540
 
7.6%
o 13704
 
7.2%
a 12567
 
6.6%
n 11031
 
5.8%
i 9868
 
5.2%
c 9654
 
5.0%
r 9450
 
4.9%
t 7703
 
4.0%
Other values (31) 66495
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129470
67.7%
Uppercase Letter 24568
 
12.8%
Space Separator 19216
 
10.0%
Other Punctuation 16984
 
8.9%
Dash Punctuation 974
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14540
11.2%
o 13704
10.6%
a 12567
9.7%
n 11031
 
8.5%
i 9868
 
7.6%
c 9654
 
7.5%
r 9450
 
7.3%
t 7703
 
5.9%
m 6213
 
4.8%
y 5876
 
4.5%
Other values (11) 28864
22.3%
Uppercase Letter
ValueCountFrequency (%)
S 6969
28.4%
A 3565
14.5%
C 2527
 
10.3%
F 2298
 
9.4%
M 2146
 
8.7%
D 1656
 
6.7%
R 1351
 
5.5%
P 1165
 
4.7%
H 989
 
4.0%
L 736
 
3.0%
Other values (7) 1166
 
4.7%
Space Separator
ValueCountFrequency (%)
19216
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16984
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154038
80.6%
Common 37174
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14540
 
9.4%
o 13704
 
8.9%
a 12567
 
8.2%
n 11031
 
7.2%
i 9868
 
6.4%
c 9654
 
6.3%
r 9450
 
6.1%
t 7703
 
5.0%
S 6969
 
4.5%
m 6213
 
4.0%
Other values (28) 52339
34.0%
Common
ValueCountFrequency (%)
19216
51.7%
, 16984
45.7%
- 974
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19216
 
10.0%
, 16984
 
8.9%
e 14540
 
7.6%
o 13704
 
7.2%
a 12567
 
6.6%
n 11031
 
5.8%
i 9868
 
5.2%
c 9654
 
5.0%
r 9450
 
4.9%
t 7703
 
4.0%
Other values (31) 66495
34.8%

Duration
Categorical

Distinct217
Distinct (%)4.2%
Missing1
Missing (%)< 0.1%
Memory size40.1 KiB
24 min. per ep.
823 
30 min. per ep.
588 
25 min. per ep.
529 
23 min. per ep.
360 
30 min.
 
120
Other values (212)
2694 

Length

Max length21
Median length15
Mean length12.697106
Min length5

Characters and Unicode

Total characters64933
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.6%

Sample

1st row24 min. per ep.
2nd row1 hr. 55 min.
3rd row24 min. per ep.
4th row25 min. per ep.
5th row23 min. per ep.

Common Values

ValueCountFrequency (%)
24 min. per ep. 823
 
16.1%
30 min. per ep. 588
 
11.5%
25 min. per ep. 529
 
10.3%
23 min. per ep. 360
 
7.0%
30 min. 120
 
2.3%
26 min. per ep. 73
 
1.4%
27 min. per ep. 65
 
1.3%
24 min. 65
 
1.3%
22 min. per ep. 64
 
1.3%
29 min. per ep. 63
 
1.2%
Other values (207) 2364
46.2%

Length

2023-06-29T02:42:18.716367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
min 5071
28.2%
ep 3271
18.2%
per 3271
18.2%
24 900
 
5.0%
30 762
 
4.2%
1 647
 
3.6%
hr 628
 
3.5%
25 590
 
3.3%
23 424
 
2.4%
26 111
 
0.6%
Other values (55) 2282
12.7%

Most occurring characters

ValueCountFrequency (%)
12843
19.8%
. 8978
13.8%
e 6550
10.1%
p 6542
10.1%
n 5074
 
7.8%
m 5071
 
7.8%
i 5071
 
7.8%
r 3899
 
6.0%
2 2903
 
4.5%
3 1638
 
2.5%
Other values (15) 6364
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32854
50.6%
Space Separator 12843
 
19.8%
Decimal Number 10257
 
15.8%
Other Punctuation 8978
 
13.8%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6550
19.9%
p 6542
19.9%
n 5074
15.4%
m 5071
15.4%
i 5071
15.4%
r 3899
11.9%
h 628
 
1.9%
s 8
 
< 0.1%
c 8
 
< 0.1%
k 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 2903
28.3%
3 1638
16.0%
4 1429
13.9%
1 1167
11.4%
5 1151
 
11.2%
0 1054
 
10.3%
6 243
 
2.4%
8 235
 
2.3%
7 233
 
2.3%
9 204
 
2.0%
Space Separator
ValueCountFrequency (%)
12843
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8978
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32855
50.6%
Common 32078
49.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6550
19.9%
p 6542
19.9%
n 5074
15.4%
m 5071
15.4%
i 5071
15.4%
r 3899
11.9%
h 628
 
1.9%
s 8
 
< 0.1%
c 8
 
< 0.1%
U 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
Common
ValueCountFrequency (%)
12843
40.0%
. 8978
28.0%
2 2903
 
9.0%
3 1638
 
5.1%
4 1429
 
4.5%
1 1167
 
3.6%
5 1151
 
3.6%
0 1054
 
3.3%
6 243
 
0.8%
8 235
 
0.7%
Other values (2) 437
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12843
19.8%
. 8978
13.8%
e 6550
10.1%
p 6542
10.1%
n 5074
 
7.8%
m 5071
 
7.8%
i 5071
 
7.8%
r 3899
 
6.0%
2 2903
 
4.5%
3 1638
 
2.5%
Other values (15) 6364
9.8%

Rating
Categorical

Distinct7
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size40.1 KiB
PG-13 - Teens 13 or older
2125 
G - All Ages
896 
Rx - Hentai
694 
R+ - Mild Nudity
494 
R - 17+ (violence & profanity)
435 
Other values (2)
469 

Length

Max length30
Median length25
Mean length19.228633
Min length7

Characters and Unicode

Total characters98316
Distinct characters40
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR - 17+ (violence & profanity)
2nd rowR - 17+ (violence & profanity)
3rd rowPG-13 - Teens 13 or older
4th rowPG-13 - Teens 13 or older
5th rowPG - Children

Common Values

ValueCountFrequency (%)
PG-13 - Teens 13 or older 2125
41.5%
G - All Ages 896
17.5%
Rx - Hentai 694
 
13.6%
R+ - Mild Nudity 494
 
9.7%
R - 17+ (violence & profanity) 435
 
8.5%
PG - Children 428
 
8.4%
Unknown 41
 
0.8%
(Missing) 2
 
< 0.1%

Length

2023-06-29T02:42:19.032370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-29T02:42:19.425378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5507
22.6%
pg-13 2125
 
8.7%
teens 2125
 
8.7%
13 2125
 
8.7%
or 2125
 
8.7%
older 2125
 
8.7%
r 929
 
3.8%
g 896
 
3.7%
all 896
 
3.7%
ages 896
 
3.7%
Other values (10) 4578
18.8%

Most occurring characters

ValueCountFrequency (%)
19214
19.5%
e 9263
 
9.4%
- 7197
 
7.3%
l 5274
 
5.4%
o 5161
 
5.2%
r 5113
 
5.2%
1 4685
 
4.8%
3 4250
 
4.3%
n 4240
 
4.3%
d 3541
 
3.6%
Other values (30) 30378
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46608
47.4%
Space Separator 19214
19.5%
Uppercase Letter 13693
 
13.9%
Decimal Number 9370
 
9.5%
Dash Punctuation 7197
 
7.3%
Math Symbol 929
 
0.9%
Other Punctuation 435
 
0.4%
Close Punctuation 435
 
0.4%
Open Punctuation 435
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9263
19.9%
l 5274
11.3%
o 5161
11.1%
r 5113
11.0%
n 4240
9.1%
d 3541
 
7.6%
s 3021
 
6.5%
i 2980
 
6.4%
t 1623
 
3.5%
a 1129
 
2.4%
Other values (11) 5263
11.3%
Uppercase Letter
ValueCountFrequency (%)
G 3449
25.2%
P 2553
18.6%
T 2125
15.5%
A 1792
13.1%
R 1623
11.9%
H 694
 
5.1%
M 494
 
3.6%
N 494
 
3.6%
C 428
 
3.1%
U 41
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 4685
50.0%
3 4250
45.4%
7 435
 
4.6%
Space Separator
ValueCountFrequency (%)
19214
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7197
100.0%
Math Symbol
ValueCountFrequency (%)
+ 929
100.0%
Other Punctuation
ValueCountFrequency (%)
& 435
100.0%
Close Punctuation
ValueCountFrequency (%)
) 435
100.0%
Open Punctuation
ValueCountFrequency (%)
( 435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60301
61.3%
Common 38015
38.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9263
15.4%
l 5274
 
8.7%
o 5161
 
8.6%
r 5113
 
8.5%
n 4240
 
7.0%
d 3541
 
5.9%
G 3449
 
5.7%
s 3021
 
5.0%
i 2980
 
4.9%
P 2553
 
4.2%
Other values (21) 15706
26.0%
Common
ValueCountFrequency (%)
19214
50.5%
- 7197
 
18.9%
1 4685
 
12.3%
3 4250
 
11.2%
+ 929
 
2.4%
& 435
 
1.1%
) 435
 
1.1%
( 435
 
1.1%
7 435
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19214
19.5%
e 9263
 
9.4%
- 7197
 
7.3%
l 5274
 
5.4%
o 5161
 
5.2%
r 5113
 
5.2%
1 4685
 
4.8%
3 4250
 
4.3%
n 4240
 
4.3%
d 3541
 
3.6%
Other values (30) 30378
30.9%

Ranked
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct396
Distinct (%)7.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4874.1768
Minimum0
Maximum11069
Zeros368
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:19.881377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12240
median4842
Q37355
95-th percentile10189
Maximum11069
Range11069
Interquartile range (IQR)5115

Descriptive statistics

Standard deviation3156.8625
Coefficient of variation (CV)0.6476709
Kurtosis-1.049347
Mean4874.1768
Median Absolute Deviation (MAD)2534
Skewness0.11585275
Sum24926540
Variance9965780.7
MonotonicityNot monotonic
2023-06-29T02:42:20.742381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 368
 
7.2%
5850 197
 
3.9%
6880 35
 
0.7%
5567 32
 
0.6%
6015 32
 
0.6%
6577 32
 
0.6%
7690 31
 
0.6%
4754 31
 
0.6%
5005 30
 
0.6%
4842 30
 
0.6%
Other values (386) 4296
84.0%
ValueCountFrequency (%)
0 368
7.2%
1 1
 
< 0.1%
7 1
 
< 0.1%
15 2
 
< 0.1%
18 1
 
< 0.1%
23 1
 
< 0.1%
28 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
11069 1
< 0.1%
11058 1
< 0.1%
11052 1
< 0.1%
11049 1
< 0.1%
11048 1
< 0.1%
11046 1
< 0.1%
11043 1
< 0.1%
11026 1
< 0.1%
11021 1
< 0.1%
11020 1
< 0.1%

Popularity
Real number (ℝ)

Distinct2385
Distinct (%)46.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5654.4411
Minimum1
Maximum14602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:21.197386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile580
Q13194.5
median5970.5
Q37731
95-th percentile10466.7
Maximum14602
Range14601
Interquartile range (IQR)4536.5

Descriptive statistics

Standard deviation3043.7889
Coefficient of variation (CV)0.53830057
Kurtosis-0.74713072
Mean5654.4411
Median Absolute Deviation (MAD)2206.5
Skewness0.024654208
Sum28916812
Variance9264650.9
MonotonicityNot monotonic
2023-06-29T02:42:21.640389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7731 452
 
8.8%
6186 126
 
2.5%
7474 46
 
0.9%
7194 39
 
0.8%
513 38
 
0.7%
7656 27
 
0.5%
5529 27
 
0.5%
6128 27
 
0.5%
1780 25
 
0.5%
6903 21
 
0.4%
Other values (2375) 4286
83.8%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
8 2
< 0.1%
15 2
< 0.1%
31 1
< 0.1%
32 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
ValueCountFrequency (%)
14602 6
0.1%
14546 2
 
< 0.1%
14383 1
 
< 0.1%
14093 1
 
< 0.1%
13825 1
 
< 0.1%
13790 5
0.1%
13664 1
 
< 0.1%
13582 1
 
< 0.1%
13501 1
 
< 0.1%
13399 1
 
< 0.1%

Members
Real number (ℝ)

Distinct2620
Distinct (%)51.4%
Missing14
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean97191.126
Minimum211
Maximum2589552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:21.994407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum211
5-th percentile965
Q14963
median131435
Q3148259
95-th percentile153848
Maximum2589552
Range2589341
Interquartile range (IQR)143296

Descriptive statistics

Standard deviation129827.2
Coefficient of variation (CV)1.3357927
Kurtosis77.794822
Mean97191.126
Median Absolute Deviation (MAD)61751
Skewness6.5466045
Sum4.9577193 × 108
Variance1.6855101 × 1010
MonotonicityNot monotonic
2023-06-29T02:42:22.318394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148259 2252
44.0%
1305 4
 
0.1%
864 3
 
0.1%
1379 3
 
0.1%
1489 3
 
0.1%
2006 3
 
0.1%
2303 3
 
0.1%
4091 3
 
0.1%
1968 3
 
0.1%
1014 3
 
0.1%
Other values (2610) 2821
55.2%
(Missing) 14
 
0.3%
ValueCountFrequency (%)
211 1
< 0.1%
220 1
< 0.1%
221 1
< 0.1%
224 1
< 0.1%
230 1
< 0.1%
237 1
< 0.1%
257 1
< 0.1%
272 1
< 0.1%
278 1
< 0.1%
294 1
< 0.1%
ValueCountFrequency (%)
2589552 1
< 0.1%
2248456 1
< 0.1%
1830540 1
< 0.1%
1591773 1
< 0.1%
1583882 1
< 0.1%
1567792 1
< 0.1%
1543765 1
< 0.1%
1352724 1
< 0.1%
1312470 1
< 0.1%
1286382 1
< 0.1%

Favorites
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct771
Distinct (%)15.2%
Missing47
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean735.1324
Minimum0
Maximum183914
Zeros464
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:22.773396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q369
95-th percentile1625.35
Maximum183914
Range183914
Interquartile range (IQR)67

Descriptive statistics

Standard deviation5865.4117
Coefficient of variation (CV)7.9787148
Kurtosis396.84124
Mean735.1324
Median Absolute Deviation (MAD)9
Skewness17.604202
Sum3725651
Variance34403055
MonotonicityNot monotonic
2023-06-29T02:42:23.015399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 464
 
9.1%
1 458
 
9.0%
2 359
 
7.0%
3 280
 
5.5%
4 228
 
4.5%
5 176
 
3.4%
6 146
 
2.9%
7 142
 
2.8%
8 104
 
2.0%
9 91
 
1.8%
Other values (761) 2620
51.2%
ValueCountFrequency (%)
0 464
9.1%
1 458
9.0%
2 359
7.0%
3 280
5.5%
4 228
4.5%
5 176
 
3.4%
6 146
 
2.9%
7 142
 
2.8%
8 104
 
2.0%
9 91
 
1.8%
ValueCountFrequency (%)
183914 1
< 0.1%
145201 1
< 0.1%
126645 2
< 0.1%
90487 2
< 0.1%
84651 1
< 0.1%
71308 1
< 0.1%
67916 1
< 0.1%
65586 1
< 0.1%
64482 1
< 0.1%
62162 1
< 0.1%

Completed
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean360068.4
Minimum1273
Maximum718161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:23.304432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1273
5-th percentile47981
Q1150183
median208333
Q3718161
95-th percentile718161
Maximum718161
Range716888
Interquartile range (IQR)567978

Descriptive statistics

Standard deviation280356.82
Coefficient of variation (CV)0.77862099
Kurtosis-1.6884261
Mean360068.4
Median Absolute Deviation (MAD)58150
Skewness0.44002927
Sum1.8413898 × 109
Variance7.8599944 × 1010
MonotonicityNot monotonic
2023-06-29T02:42:23.499401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
718161 1913
37.4%
150183 1734
33.9%
208333 875
17.1%
47981 472
 
9.2%
1273 73
 
1.4%
44663 47
 
0.9%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1273 73
 
1.4%
44663 47
 
0.9%
47981 472
 
9.2%
150183 1734
33.9%
208333 875
17.1%
718161 1913
37.4%
ValueCountFrequency (%)
718161 1913
37.4%
208333 875
17.1%
150183 1734
33.9%
47981 472
 
9.2%
44663 47
 
0.9%
1273 73
 
1.4%

On-Hold
Real number (ℝ)

Distinct153
Distinct (%)3.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7356.0368
Minimum8
Maximum187919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:23.835402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile112
Q1712
median1935
Q31935
95-th percentile71513
Maximum187919
Range187911
Interquartile range (IQR)1223

Descriptive statistics

Standard deviation19688.848
Coefficient of variation (CV)2.6765565
Kurtosis17.939216
Mean7356.0368
Median Absolute Deviation (MAD)1186
Skewness3.8579424
Sum37618772
Variance3.8765073 × 108
MonotonicityNot monotonic
2023-06-29T02:42:24.389407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1935 1834
35.9%
112 557
 
10.9%
71513 352
 
6.9%
749 291
 
5.7%
712 281
 
5.5%
145 273
 
5.3%
3124 187
 
3.7%
595 128
 
2.5%
11901 114
 
2.2%
766 109
 
2.1%
Other values (143) 988
19.3%
ValueCountFrequency (%)
8 1
 
< 0.1%
10 2
< 0.1%
14 2
< 0.1%
16 1
 
< 0.1%
29 4
0.1%
30 4
0.1%
40 1
 
< 0.1%
47 2
< 0.1%
49 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
187919 9
 
0.2%
130961 1
 
< 0.1%
109707 1
 
< 0.1%
108697 1
 
< 0.1%
87145 1
 
< 0.1%
71513 352
6.9%
61734 1
 
< 0.1%
54480 1
 
< 0.1%
53682 3
 
0.1%
47488 5
 
0.1%

Dropped
Real number (ℝ)

Distinct1372
Distinct (%)26.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1340.4998
Minimum0
Maximum174710
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2023-06-29T02:42:24.659413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q181
median154.5
Q3490.75
95-th percentile5575.6
Maximum174710
Range174710
Interquartile range (IQR)409.75

Descriptive statistics

Standard deviation5878.2583
Coefficient of variation (CV)4.3851243
Kurtosis347.18969
Mean1340.4998
Median Absolute Deviation (MAD)96.5
Skewness15.518171
Sum6855316
Variance34553920
MonotonicityNot monotonic
2023-06-29T02:42:24.907409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 37
 
0.7%
78 33
 
0.6%
77 33
 
0.6%
94 33
 
0.6%
74 33
 
0.6%
70 32
 
0.6%
54 32
 
0.6%
42 32
 
0.6%
45 32
 
0.6%
79 32
 
0.6%
Other values (1362) 4785
93.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
14 1
 
< 0.1%
19 2
 
< 0.1%
21 4
 
0.1%
23 1
 
< 0.1%
24 2
 
< 0.1%
25 4
 
0.1%
26 1
 
< 0.1%
27 11
0.2%
28 1
 
< 0.1%
ValueCountFrequency (%)
174710 1
< 0.1%
148408 1
< 0.1%
136245 1
< 0.1%
124253 1
< 0.1%
99806 1
< 0.1%
80834 1
< 0.1%
65962 1
< 0.1%
47875 1
< 0.1%
47273 1
< 0.1%
44157 1
< 0.1%

Interactions

2023-06-29T02:42:03.485211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:10.775681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:15.659711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:18.604274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:22.753303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:28.534344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:31.574922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:34.665948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:39.667992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:43.566540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:46.794563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:50.398588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:55.283622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:00.086189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:03.725207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:11.171684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:15.881716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:18.819278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:23.197305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:28.756347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:31.789923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:34.991958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:40.109982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:43.790550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:47.024567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:50.710590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:55.708625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:00.329193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:03.936209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:11.549686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:16.077717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:19.034277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:23.583308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:28.960347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:31.991925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:35.539945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:40.509981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:43.993563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:47.237566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:50.955593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:56.147626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:00.568183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:04.185209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:11.955689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:16.290728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:19.259298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:23.978311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:29.189354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:32.198936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:35.740021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:40.939523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:44.220547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:47.460571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:51.209593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:56.613631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:00.809187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:04.510209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:12.400692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:16.502722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:19.474283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:24.416313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:29.403352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:32.536927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:35.952959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:41.332524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:44.443560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:47.671583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:51.475594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:57.078646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:01.076189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:04.760217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:12.846695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:16.710724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:19.687282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:26.505326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:29.620353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:32.750928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:36.173960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:41.551526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:44.675553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:47.864574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:51.683609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:57.517636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:01.323204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:05.012213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:13.298697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:16.902723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:19.887286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:26.702329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:29.796354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:32.940930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:36.398953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:41.746529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:44.887552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:48.054576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:51.951602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:58.026638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:01.544197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:05.377220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:13.694707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:17.109740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:20.112283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:26.944334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:30.013359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:33.144932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:36.781957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:41.959533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:45.109556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:48.267574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:52.196603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:58.358651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:01.771205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:05.601220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:14.243749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:17.296723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:20.315289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:27.159338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:30.221361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:33.352934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:37.166958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:42.176544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:45.332558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:48.915581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:52.503603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:58.590644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:02.018193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:06.493224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:14.508703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:17.508730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:20.668288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:27.382338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:30.443359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:33.565933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:37.596976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:42.421541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:45.566557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:49.153581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:52.944604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:58.833648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:02.264200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:06.717224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:14.726712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:17.699732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:21.105291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:27.614340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:30.668365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:33.769938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:37.958965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:42.637535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:45.847561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:49.380586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:53.359607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:59.052651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:02.502200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:07.061224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:14.965710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:17.917282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:21.536293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:27.830340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:30.887361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:33.986938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:38.389967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:42.864539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:46.111562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:49.609582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:53.874625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:59.302651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:02.743201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:07.343225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:15.190712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:18.140276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:21.970297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:28.052361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:31.126364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:34.231946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:38.790973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:43.093534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:46.340561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:49.867587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:54.383616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:59.570651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:02.983202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:07.554231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:15.429716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:18.367276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:22.358300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:28.305345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:31.353368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:34.441944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:39.200976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:43.342541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:46.577560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:50.134586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:54.838614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:41:59.813181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-06-29T02:42:03.244208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-06-29T02:42:25.575413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
anime_idmy_scoreuser_idscorescored_byrankpopularityRankedPopularityMembersFavoritesCompletedOn-HoldDroppedgendertypesourceRating
anime_id1.000-0.0620.277-0.0500.0120.058-0.0470.001-0.003-0.0010.010-0.026-0.040-0.0010.0440.0950.1170.019
my_score-0.0621.000-0.0520.2420.240-0.239-0.219-0.0010.011-0.020-0.009-0.0040.011-0.0150.0880.0670.0520.000
user_id0.277-0.0521.0000.0490.122-0.044-0.130-0.025-0.0100.0460.023-0.029-0.0230.0110.1470.0340.0440.036
score-0.0500.2420.0491.0000.639-1.000-0.649-0.015-0.0130.0070.0170.013-0.0080.0170.0550.1540.1330.000
scored_by0.0120.2400.1220.6391.000-0.622-0.989-0.007-0.0110.0230.0180.013-0.0030.0100.0160.1550.1050.012
rank0.058-0.239-0.044-1.000-0.6221.0000.6320.0130.014-0.009-0.016-0.0080.007-0.0130.0140.1410.1360.000
popularity-0.047-0.219-0.130-0.649-0.9890.6321.0000.0080.014-0.023-0.024-0.0130.004-0.0150.0460.2650.1680.000
Ranked0.001-0.001-0.025-0.015-0.0070.0130.0081.0000.438-0.204-0.535-0.221-0.120-0.4350.0420.0080.0000.171
Popularity-0.0030.011-0.010-0.013-0.0110.0140.0140.4381.000-0.268-0.618-0.206-0.090-0.5610.0130.0000.0000.259
Members-0.001-0.0200.0460.0070.023-0.009-0.023-0.204-0.2681.0000.2720.0190.0260.2450.0720.0160.0000.053
Favorites0.010-0.0090.0230.0170.018-0.016-0.024-0.535-0.6180.2721.0000.2640.0340.7730.0670.0000.0000.027
Completed-0.026-0.004-0.0290.0130.013-0.008-0.013-0.221-0.2060.0190.2641.0000.1760.4750.0180.0140.0000.285
On-Hold-0.0400.011-0.023-0.008-0.0030.0070.004-0.120-0.0900.0260.0340.1761.0000.0210.0790.0000.0000.054
Dropped-0.001-0.0150.0110.0170.010-0.013-0.015-0.435-0.5610.2450.7730.4750.0211.0000.0830.0140.0000.030
gender0.0440.0880.1470.0550.0160.0140.0460.0420.0130.0720.0670.0180.0790.0831.0000.0210.0390.025
type0.0950.0670.0340.1540.1550.1410.2650.0080.0000.0160.0000.0140.0000.0140.0211.0000.2600.009
source0.1170.0520.0440.1330.1050.1360.1680.0000.0000.0000.0000.0000.0000.0000.0390.2601.0000.000
Rating0.0190.0000.0360.0000.0120.0000.0000.1710.2590.0530.0270.2850.0540.0300.0250.0090.0001.000

Missing values

2023-06-29T02:42:08.010228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-29T02:42:08.935237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-29T02:42:09.867779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

usernameanime_idmy_scoreuser_idgendertitletypesourcescorescored_byrankpopularitygenreDurationRatingRankedPopularityMembersFavoritesCompletedOn-HoldDropped
0karthiga2192255153FemaleOne PieceTVManga8.54423868.091.035Action, Adventure, Comedy, Super Power, Drama, Fantasy, Shounen24 min. per ep.R - 17+ (violence & profanity)28.039.01251960.061971.0718161.071513.026678.0
1RedvelvetDaisuki91801897606FemaleGintamaTVManga9.01141830.015.095Action, Sci-Fi, Comedy, Historical, Parody, Samurai, Shounen1 hr. 55 min.R - 17+ (violence & profanity)159.0518.0273145.01174.0208333.01935.0770.0
2RedvelvetDaisuki947931897606FemaleCoppelionTVManga6.5437299.05443.0949Action, Sci-Fi, Seinen24 min. per ep.PG-13 - Teens 13 or older266.0201.0558913.012944.0718161.071513.013925.0
3RedvelvetDaisuki2213581897606FemalePing Pong The AnimationTVManga8.6572887.058.0563Sports, Psychological, Drama, Seinen25 min. per ep.PG-13 - Teens 13 or older2481.01467.094683.0587.0718161.071513.05378.0
4Damonashu1829637326MaleGedo SenkiMovieNovel7.1344087.03072.01125Adventure, Fantasy, Magic23 min. per ep.PG - Children3710.04369.013224.018.0718161.0766.01108.0
5Damonashu1921537326MaleUrusei Yatsura Movie 2: Beautiful DreamerMovieManga7.873574.0769.04080Action, Adventure, Comedy, Romance, Drama, Sci-Fi23 min. per ep.PG-13 - Teens 13 or older604.01003.0148259.02066.0718161.014228.011573.0
6bskai2256228342MaleDragon Ball GTTVManga6.67205925.04883.0204Action, Adventure, Comedy, Fantasy, Magic, Sci-Fi, Shounen, Super Power23 min. per ep.PG-13 - Teens 13 or older468.0687.0214499.04101.0718161.011901.011026.0
7bskai17516228342MaleJikuu Bouken NuumamonjaaOVAUnknown5.333347.08633.04650Action, Comedy, Fantasy23 min. per ep.PG-13 - Teens 13 or older1317.03612.0148259.0231.0718161.0766.01168.0
8Slimak3407061677MaleBlassreiterTVOriginal7.0321867.03443.01271Action, Sci-Fi27 min. per ep.PG-13 - Teens 13 or older360.01233.0148259.0979.0718161.011901.01356.0
9Slimak6547061677MaleAngel Beats!TVOriginal8.31641851.0227.07Action, Comedy, Drama, School, Supernatural24 min. per ep.R+ - Mild Nudity30.0169.0614100.029436.0718161.047488.023008.0
usernameanime_idmy_scoreuser_idgendertitletypesourcescorescored_byrankpopularitygenreDurationRatingRankedPopularityMembersFavoritesCompletedOn-HoldDropped
5105alice707102987249424FemaleKaichou wa Maid-sama!: Goshujinsama to Asonjao♥SpecialManga7.4127379.01975.01506Comedy, School, Shoujo24 min. per ep.PG-13 - Teens 13 or older2116.01232.0113530.0547.0718161.0712.05113.0
5106Sam_J1604726952MaleKatekyo Hitman Reborn!TVManga8.31145865.0230.0173Action, Comedy, Shounen, Super Power1 hr. 22 min.G - All Ages6493.011493.0862.016.0208333.01935.055.0
5107Sam_J31964826952MaleBoku no Hero AcademiaTVManga8.44494037.0137.027Action, Comedy, School, Shounen, Super Power25 min.PG-13 - Teens 13 or older6646.08644.01701.01.0208333.01935.075.0
5108Sam_J265926952MaleHajime no Ippo: Mashiba vs. KimuraOVAManga8.2734023.0264.01472Comedy, Shounen, Sports52 min.PG-13 - Teens 13 or older7178.08644.01107.00.0208333.01935.050.0
5109Sam_J32696626952MaleFukigen na MononokeanTVWeb manga7.4526464.01804.01253Comedy, Demons, Supernatural15 min. per ep.PG-13 - Teens 13 or older8281.06914.0148259.08.0150183.0112.0100.0
5110nikekid9387343768295MaleTsukuyomi: Moon PhaseTVManga7.1021039.03218.01475Comedy, Romance, Vampire, Fantasy, Seinen15 min.PG-13 - Teens 13 or older3968.07185.0148259.012.0150183.01935.0104.0
5111Retridemption8850531285FemaleTenshi no TamagoOVAOriginal7.6320757.01282.01478Fantasy, Dementia, Drama24 min. per ep.PG-13 - Teens 13 or older278.03486.0490554.08762.0718161.012737.016529.0
5112Retridemption23697531285FemaleRental MagicaTVLight novel7.1415714.03053.01620Mystery, Supernatural, Fantasy15 min. per ep.PG - Children5850.012764.0382.00.0718161.0296.051.0
5113Retridemption428210531285FemaleKara no Kyoukai 5: Mujun RasenMovieLight novel8.6378701.065.0594Action, Mystery, Supernatural, Drama, Romance, Thriller16 min.PG - Children5850.06656.0335.00.0208333.01935.0118.0
5114Retridemption21556531285FemaleKono Aozora ni Yakusoku wo: Youkoso Tsugumi Ryou eTVVisual novel6.304448.06396.03058Harem, Romance, SchoolNaNNaNNaNNaNNaNNaNNaNNaNNaN